Nicotinamide‐N‐methyltransferase is a promising metabolic drug target for primary and metastatic clear cell renal cell carcinoma

Abstract Background The metabolic enzyme nicotinamide‐N‐methyltransferase (NNMT) is highly expressed in various cancer entities, suggesting tumour‐promoting functions. We systematically investigated NNMT expression and its metabolic interactions in clear cell renal cell carcinoma (ccRCC), a prominent RCC subtype with metabolic alterations, to elucidate its role as a drug target. Methods NNMT expression was assessed in primary ccRCC (n = 134), non‐tumour tissue and ccRCC‐derived metastases (n = 145) by microarray analysis and/or immunohistochemistry. Findings were validated in The Cancer Genome Atlas (kidney renal clear cell carcinoma [KIRC], n = 452) and by single‐cell analysis. Expression was correlated with clinicopathological data and survival. Metabolic alterations in NNMT‐depleted cells were assessed by nontargeted/targeted metabolomics and extracellular flux analysis. The NNMT inhibitor (NNMTi) alone and in combination with the inhibitor 2‐deoxy‐D‐glucose for glycolysis and BPTES (bis‐2‐(5‐phenylacetamido‐1,3,4‐thiadiazol‐2‐yl)ethyl‐sulfide) for glutamine metabolism was investigated in RCC cell lines (786‐O, A498) and in two 2D ccRCC‐derived primary cultures and three 3D ccRCC air–liquid interface models. Results NNMT protein was overexpressed in primary ccRCC (p = 1.32 × 10–16) and ccRCC‐derived metastases (p = 3.92 × 10–20), irrespective of metastatic location, versus non‐tumour tissue. Single‐cell data showed predominant NNMT expression in ccRCC and not in the tumour microenvironment. High NNMT expression in primary ccRCC correlated with worse survival in independent cohorts (primary RCC—hazard ratio [HR] = 4.3, 95% confidence interval [CI]: 1.5–12.4; KIRC—HR = 3.3, 95% CI: 2.0–5.4). NNMT depletion leads to intracellular glutamine accumulation, with negative effects on mitochondrial function and cell survival, while not affecting glycolysis or glutathione metabolism. At the gene level, NNMT‐depleted cells upregulate glycolysis, oxidative phosphorylation and apoptosis pathways. NNMTi alone or in combination with 2‐deoxy‐D‐glucose and BPTES resulted in inhibition of cell viability in ccRCC cell lines and primary tumour and metastasis‐derived models. In two out of three patient‐derived ccRCC air–liquid interface models, NNMTi treatment induced cytotoxicity. Conclusions Since efficient glutamine utilisation, which is essential for ccRCC tumours, depends on NNMT, small‐molecule NNMT inhibitors provide a novel therapeutic strategy for ccRCC and act as sensitizers for combination therapies.

1. NNMT is highly expressed in ccRCC primary tumours and metastases, correlating with worse patient survival. 2. Glutamine metabolism is impaired in NNMT-depleted cells, with negative consequences for cellular fitness. 3. NNMT inhibition reduces cell viability and induces cytotoxicity in 2D/3D ccRCC-derived tumour models. 4. Beyond NNMT inhibition for the treatment of metabolic diseases, its application for anticancer therapy appears promising.

BACKGROUND
Nicotinamide-N-methyltransferase (NNMT) is a phase II metabolising enzyme that is mainly expressed in the liver but also in other organs, including the kidneys. It catalyses the transfer of a methyl group from S-adenosylmethionine (SAM) to a broad range of substrates, forming S-adenosylhomocysteine and the methylated substrate. With this methylation reaction, NNMT facilitates the excretion of its substrates, which was long considered the main function of NNMT. However, studies in recent years have shown increased expression of NNMT in various types of cancer, including clear cell renal cell carcinoma (ccRCC), 1,2 implying functions of this enzyme in tumour development and/or growth. 3 The mechanisms of increased expression of NNMT in cancer are not completely understood; however, induction by hepatocyte nuclear factor-1β (HNF1β), 4 signal transducers and activators of transcription 3 (STAT3), 5 or transforming growth factor-β (TGFβ) 6 appears to be involved. Similarly, the functions proposed for NNMT in cancer are diverse. High NNMT expression was shown to increase replication, 7 migration and invasion, 8,9 as well as adenosine triphosphate (ATP) synthesis 10,11 in cancer cells. Moreover, by consuming SAM and generating a methylation sink within cells, NNMT inhibits the activities of other methyltransferases, leading to decreased histone and DNA methylation and consequently altered gene expression in cancer cells. 12,13 Furthermore, NNMT withdraws nicotinamide, its preferred substrate, from the nicotinamide adenine dinucleotide (NAD + ) salvage pathway that provides NADH for mito-chondrial energy production. In ccRCC, NNMT-derived peptides are human leucocyte antigen (HLA) dependently presented on the surface of tumour cells. 14 Taken together, NNMT might have miscellaneous effects on cancer cell metabolism that act together to support tumourigenesis and invasive cancer growth. The entanglement of NNMT with cellular metabolism makes it particularly interesting for a comprehensive study in ccRCC, the most common subtype of renal cancer. ccRCC can be considered a metabolic disease, since loss of chromosome arm 3p and inactivation of the Von-Hippel Lindau (VHL) tumour suppressor and the resultant constitutively active hypoxia-inducible factor (HIF) transcription factor in the majority of tumours lead to marked metabolic changes. [15][16][17] These changes include upregulation of aerobic glycolysis to form lactate (Warburg effect), the pentose phosphate pathway, fatty acid synthesis, glutamine and glutathione (GSH) metabolism, and downregulation of the tricarboxylic acid (TCA) cycle, fatty acid β-oxidation and oxidative phosphorylation (oxphos). 18 In addition, constitutive activation of HIF imitates a hypoxic environment and induces the formation of new blood vessels (neo-angiogenesis) in the growing tumour. Antiangiogenic agents, together with inhibitors of the mechanistic target of rapamycin (mTOR) pathway, that are affected by activating mutations in ccRCC, and immune checkpoint inhibitors are therefore used in the clinical management of advanced and metastatic ccRCC. 19,20 Initial response rates of single agents range approximately 30%, and usually patients are treated in several treatment lines with agents from the different classes. Nevertheless, due to resistance development and tumour recurrence, metastatic ccRCC is rarely cured, and only 13% of those patients survive 5 years or more after diagnosis. 21 To increase response rates and to extend the survival of patients with advanced or metastatic ccRCC, new drug targets from alternative cellular pathways that can be combined with already existing therapies are urgently needed.
Therefore, we investigated NNMT in primary ccRCC and ccRCC-derived metastasis in an effort to better understand its role in tumour metabolism and to elucidate its eligibility as a metabolic drug target. For in vitro experiments, we used the established ccRCC-derived cell lines 786-O and A498 and two newly established ccRCC-derived primary cultures (RCC1 and RCC2). In addition, ex vivo 3D air-liquid interface (ALI) models 22 of ccRCC were used to test the NNMT inhibitor 5-amino-1-methylquinolium (NNMTi). 23

Patient cohorts
We studied NNMT expression in two primary ccRCC cohorts and one cohort comprising ccRCC-derived metastases (Table 1). Cohort 1 contained 134 ccRCC tissues that were treated at the Department of Urology, University Hospital Tuebingen, Germany. A second cohort consisted of 145 ccRCC-derived metastases from 78 patients (cohort 2), collected again at the Department of Urology, University Hospital Tuebingen, Germany. For these patients, formalin-fixed and paraffin-embedded tissues were available for 64 primary ccRCC and 50 paired non-tumour tissue samples (cohort 1), as well as all 145 metastatic ccRCC sections of cohort 2. Fresh-frozen tissues from cohort 1 (n = 124) were available for mRNA expression analysis. Clinicopathological data for both cohorts were collected at the Department of Urology at the University Hospital Tuebingen and evaluated independently by at least two urologists. Data were analysed retrospectively in this study. In addition, the kidney renal clear cell carcinoma (KIRC) cohort of The Cancer Genome Atlas (TCGA KIRC cohort), containing 452 ccRCC tissues and 67 paired nontumour tissues, was analysed. Clinical and transcriptome data were downloaded from the Genomic Data Commons Portal. 24

NNMT immunohistochemical staining
Tissue microarrays were processed as described. 25 NNMT protein was stained with a monoclonal antibody (Santa Cruz Biotechnology Cat# SC-376048) at a 1:50 dilution. The stained slides were scanned with an SCN400 slide scanner (Leica Microsystems). Cellular NNMT staining was analysed with the software-based image analysis system Tis-sueStudio (Definiens AG). The mean chromogen intensities in the tumour and non-tumour areas were used as NNMT expression values. If more than one core was available per tissue, the mean expression was calculated. The antibody used in immunohistochemical (IHC) was validated by siRNA knockdown and Western blot ( Figure  S1A). Knockdown was performed as described below. For Western blotting, cells were harvested in sodium phosphate buffer containing pefabloc (Carl Roth Cat# A154.1) and protease inhibitor cocktail (Sigma, Cat# P8340). Cells were lysed by sonication, and membrane and cytoplasmic components were separated by centrifugation. For Western blotting, 10 μg of total protein was loaded per lane. Proteins were transferred to a nitrocellulose membrane by semidry transfer. Primary NNMT antibody was diluted 1:100 in 3% skim milk in Tris-buffered saline with Tween 20 (TBST) and incubated at 4 • C overnight. The β-actin antibody (Sigma Cat# A4551) and secondary antibody (Santa Cruz Biotechnology Cat# sc-2031) were diluted 1:5000 in 3% skim milk in TBST and incubated for 1 h at room temperature.

mRNA extraction and analysis
Tissue mRNA was extracted and analysed by HTA 2.0 microarrays (Thermo Fisher Scientific Cat# 902162) as previously described. 26

Analysis of single-cell gene expression data
The single-cell gene expression data set from Young et al. 32 was downloaded from the paper's supplement and  Figure S1B,C).

Metabolomics analysis
For metabolomics analysis, approximately 1 × 10 6 cells were harvested 48 h after transfection. Four hours prior to harvest, the culture medium was exchanged with Opti-MEM reduced-serum medium (Thermo Fisher Scientific Cat# 51985026). Cells were detached with StemPro Accutase (Thermo Fisher Scientific Cat# A1110501) and collected by centrifugation at 4 • C. Pellets were flash-frozen in liquid nitrogen and kept at -80 • C until metabolite extraction. Metabolites were extracted using 80% organic solvent as described. 37,38 In brief, pellets were taken up in 150 μl ice cold methanol:acetonitrile:water (2:2:1, v/v/v) and lysed by three cycles of flash-freezing, thawing and ultrasonic treatment. Afterwards, lysates were frozen for 1 h at -20 • C, and debris was removed by centrifugation. The remaining supernatant was transferred to a new vial and dried by evaporation with N 2 gas. For nontargeted metabolomics analysis, extracts were reconstituted in 100 μl water:acetonitrile (5:95, v/v) and analysed by hydrophilic interaction liquid chromatography on a 1290 Infinity ultrahigh-performance liquid chromatography system coupled to a 6550 iFunnel quadrupole timeof-flight mass spectrometer (LC-QTOF-MS) from Agilent Technologies as previously described. 39 For targeted analysis, pellets were resuspended in 50 μl of methanol:water (1:1, v/v). Intracellular and extracellular concentrations of pyruvate, fumarate, malate, α-ketoglutarate, hydroxyglutarate, citrate and proteinogenic amino acids were determined by gas chromatography-mass spectrometry as described previously. 40,41 Ornithine and citrulline were quantified by liquid chromatography with tandem mass spectrometry (LC-MS-MS) analysis similar to a published method. 42

Cell viability assay
Cell viability was assessed with the RealTime-Glo MT Cell Viability Assay (Promega Cat# G9711) according to the manufacturer's instructions.

Glutathione assay
Levels of reduced and oxidised glutathione (GSSG) were quantified with the GSH/GSSG-Glo Assay (Promega Cat# V6611) according to the manufacturer's instructions.

Gene set enrichment analysis
Gene set enrichment analysis (GSEA) was carried out in R/RStudio 33,34 with the additional packages GSVA (method 'ssgsea', version 1.32.0) 43 and piano (version 2.0.2). 44 For piano GSEA, fold-chances between NNMT kd and ctr.1 cells were used as input, and the 'gsea' method was chosen as gene set statistics (geneSetStat). Hallmark and glutamine-related gene signatures were retrieved from the molecular signatures database MsigDB. 45

Data analysis and statistics
Analyses of data from cell culture experiments were performed in R/RStudio 33,34 with the additional packages beeswarm (version 0.2.3) and ggplot2 (version 3.3.5) and in GraphPad Prism (GraphPad Software Inc., version 5.04).
In the nontargeted metabolomics experiment, we applied Welch's tests for comparisons between NNMT kd cells and each of the three controls (Table S1). The unadjusted pvalues were used as criteria to select significantly regulated metabolites in NNMT knockdown cells compared to controls. Information regarding the adjustment of pvalues by the Benjamini-Hochberg method to correct for multiple testing is given in Table S1. Data from the targeted metabolomics analysis experiment and the GSH assay were investigated by repeated measures analysis of variance and post hoc Tukey's range test. For extracellular flux analyses and inhibitor experiments, we calculated the pooled standard error of replicate experiments and used the pt() function in R to calculate corresponding p-values.
Here, p-values were corrected for multiple testing with the Benjamini-Hochberg method. For clinical data, Wilcoxon signed rank and rank sum tests were used to check for differences in paired and unpaired samples, respectively. Survival analysis was performed with the packages party (version 1.3-5) and survival (version 3.2-7) in R/RStudio. Optimal cut-offs and corresponding p-values were determined by conditional interference tree models (ctree function). Cox proportional hazard regression models were used to estimate hazard ratios (HR) and corresponding confidence intervals. Survival functions were estimated by Kaplan-Meier curves. All statistical tests were two sided, and the significance level was set to 5%.

NNMT is highly expressed in primary ccRCC and metastases and associated with inferior patient survival
NNMT protein assessed by IHC staining was significantly more highly expressed in primary ccRCC than in nontumour tissue of cohort 1, in unpaired ( Figure 1A, n = 64, fold-changes [fc] = 3.58, p = 1.32 × 10 -16 ) as well as in paired samples ( Figure 1A, n = 44, fc = 3.96, p = 3.41 × 10 -13 ). A significant correlation between mRNA and protein expression was found for the ccRCC samples (Spearman correlation, R = 0.30, p = .03). Higher mRNA expression was also found for tumour samples of the TCGA KIRC cohort compared to non-tumour tissue in unpaired ( Figure 1B High NNMT expression in primary ccRCC correlated with advanced disease grade (p = .030), stage (p = .022) and the presence of distant metastases (p = .006) in the TCGA KIRC cohort (n = 452) but not in primary ccRCC cohort 1 ( Figure S2).
Analysis of a publicly available single-cell gene expression data set 32 showed the highest expression of NNMT in the tumour epithelium and vascular compartment compared to foetal and adult normal kidney compartments and the normal and tumour immune compartments ( Figure 1C). Gene expression-based clustering of single cells and cluster annotation in two RCC samples part of the study by Young et al. revealed tumour cells, endothelial cells, tissue stem cells and various immune cell populations in the RCC samples ( Figure 1D). NNMT was again most highly expressed in the tumour cell cluster, confirming its prominent expression in ccRCC tumour tissue ( Figure 1E).
NNMT protein was also highly expressed in ccRCCderived metastases (cohort 2, n = 145), with bone and lung metastases showing the highest expression levels ( Figure 1F). Metastatic NNMT expression was significantly higher than that in non-tumour tissue ( Figure 1F, fc = 2.15, p = 3.92 × 10 -20 ) and slightly lower than that in primary RCC ( Figure 1F, fc = 0.60, p = 1.46 × 10 -6 ). From 44 patients in our metastasis cohort, we had more than one metastatic tissue available. Analysis of these tissues showed that NNMT protein expression varied within metastases from the same patient, independent of metastasis location or time after initial cancer diagnosis ( Figure S3A). Furthermore, cohort 2 included four pairs of metastases representing metastatic progression. In two of those cases, NNMT protein expression was higher in the progressed metastasis than in the initially resected metastasis ( Figure S3B). Generally, there was no increase in NNMT protein expression in metastases diagnosed at later versus earlier time points after the initial cancer diagnosis ( Figure S3C).
We did not observe altered NNMT protein expression in metastases pretreated with tyrosine kinase inhibitors (TKIs) (Figure 1H), the most frequently applied systemic therapy in our metastatic patient cohort 2. In three of four patients with metastases resected before and after systemic therapy, NNMT expression was lower after TKI treatment ( Figure 1I). An overview of systemic treatments in cohort 2 is given in Table 2.

NNMT knockdown affects glutamine metabolism and cell viability in RCC cells
To investigate metabolic changes that may occur in cells with depleted NNMT expression, we chose a nontargeted metabolomics approach. Therefore, we performed siRNA-mediated knockdown of NNMT in the 786-O renal cell carcinoma cell line (NNMT kd ) and analysed the cell lysates by LC-QTOF-MS. Of all detected features, 35 features were differentially regulated (fc > 1.2, p < .05) in NNMT kd cells compared to controls (Table S1). Furthermore, cells with depleted NNMT expression formed a separate cluster when analysed by principal compo-   Figure 2B). Glutamine is a substrate for various cellular pathways, ranging from DNA/RNA and protein synthesis to energy and GSH metabolism ( Figure 2C). Notably, ccRCC relies on glutamine for tumour growth, 47  advanced/metastatic ccRCC. 48 The finding of increased levels of glutamine in NNMT kd cells raised the question of whether NNMT might impact glutamine metabolism in ccRCC, with potential consequences on the fitness and survival of tumour cells. To assess the effect of NNMT knockdown on the fitness of 786-O cells, we analysed cell viability. Indeed, viability in NNMT kd cells was decreased by 18.3% compared to UT controls and by 12.7% compared to nontargeting siRNA-transfected control cells (NNMT kd vs. ctr.1, p (t-test) = .0315) ( Figure 2D).

Targeted metabolomics analysis of amino acids and TCA cycle intermediates confirms the accumulation of glutamine in NNMT kd cells
To further investigate the consequences of NNMT knockdown on cellular (glutamine) metabolism, we performed a targeted metabolomics analysis, assessing intra-and extracellular levels of the different amino acids and TCA cycle intermediates (Table S2). In this setting, the accumulation of glutamine in NNMT kd cells was confirmed (fc = 3.6, p (Tukey) = .029). Interestingly, the levels of most other amino acids were also increased in NNMT kd cells, together with the TCA cycle intermediates malate (fc = 1.5, p (Tukey) = .007), fumarate (fc = 1.5, p (Tukey) = .004) and α-ketoglutarate (fc = 1.9, p (Tukey) = .011) ( Figure 3A). NNMT kd cells secreted increased amounts of glutamine (fc = 1.4, p (Tukey) = .021), glycine (fc = 3.2, p (Tukey) = .025), alanine (fc = 1.2, p (Tukey) = .012), α-ketoglutarate (fc = 1.6, p (Tukey) < .001) and citrate (fc = 1.5, p (Tukey) = .002) ( Figure 3B). Furthermore, NNMT kd cells took up increased levels of aspartate (fc = 1.3, p (Tukey) = .014) from the media and less pyruvate (fc = 0.7, p (Tukey) = .045) and tyrosine (fc = 0.7, p (Tukey) = .019) ( Figure 3C). An overview of the metabolic changes in NNMT kd cells is given in Figure 3D. We suspected the increased uptake of aspartate by NNMT kd cells to be a mechanism to compensate for impaired glutamine metabolism in those cells, as aspartate is able to substitute for glutamine under glutaminedeprived conditions. 49 If this held true, cultivation in media with sufficient aspartate would rescue NNMT kd cell viability. To test this hypothesis, we cultured NNMT kd and control cells in glucose-and amino acid-free media supplied with glutamine and/or aspartic acid. However, supplementation with aspartic acid did not rescue the viability of NNMT kd cells ( Figure S4A). Of note, the addition of glucose to the media had only a small benefit for cell viability when glutamine was present, showing the dependency of 786-O cells on glutamine for cell survival, as described before 47,50 ( Figure S4B). Importantly, glutamine accumulation in NNMT kd cells did not lead to diminished levels of TCA cycle intermediates or amino acid synthesis, suggesting defects in other pathways that cause the observed impaired cell viability of NNMT kd cells. In fact, accumulation of those metabolites could be a consequence of reduced viability, accompanied by a decreased demand for macromolecular synthesis.

Glutathione levels are unaffected in NNMT kd cells, while levels of oxidised glutathione are diminished
Glutamine is an important substrate for the synthesis of GSH ( Figure 2C), which is needed to neutralise reactive oxygen species (ROS) that are generated by the electron transport chain of mitochondrial respiration. Especially in high-grade ccRCC, the antioxidant response mediated by GSH becomes essential for tumour growth. 50 We suspected that the reduced cell viability of NNMT kd cells might be caused by impaired GSH-mediated ROS neutralisation due to defective GSH synthesis in those cells.
To assess this, we measured the levels of reduced GSH and the GSSG in NNMT kd and control cells. Interestingly, GSH levels were unchanged in NNMT knockdown conditions ( Figure 4A), whereas levels of GSSG were significantly decreased by 26.5% (p (Tukey) = .001) and 17.2% (p (Tukey) = .007) when compared to UT and ctr.1 control cells, respectively ( Figure 4B). These results indicate that NNMT knockdown does not affect GSH synthesis. The observed decrease in GSSG levels might indicate a decreased demand for ROS neutralisation in NNMT kd cells.

NNMT kd cells have impaired mitochondrial function
Since ROS are primarily produced during mitochondrial oxphos, diminished oxphos could explain the decreased levels of GSSG in NNMT kd cells. To investigate the impact of NNMT knockdown on mitochondrial function, we performed extracellular flux analysis with mitochondrial modulators (Mito Stress Test, Agilent) in NNMT kd and control cells. For comprehensiveness, we also looked at glycolytic function (Glyco Stress Test, Agilent), the second major cellular energy production pathway. NNMT knockdown did not affect glycolysis in 786-O cells ( Figure  S5). In contrast, mitochondrial respiration was indeed impaired in NNMT kd cells, especially under stressed conditions that are mimicked by the uncoupling agent trifluoromethoxy carbonylcyanide phenylhydrazone (FCCP) ( Figure 5A). Consequently, maximal respiration (diff ( Figure S6). The assay medium used contains many possible energy sources, including glucose, fatty acids, glutamine and other amino acids, as possible mitochondrial fuels. To assess which fuel sources are used in our setting, we performed the Mito Fuel Flex Test assay (Agilent). In this assay, cells' dependencies on glucose, glutamine and long-chain fatty acids are measured, as well as their capacity to use one of the fuel sources if the other two pathways are inhibited. 786-O cells depend mainly on glucose for mitochondrial respiration, with no apparent differences between NNMT kd and control cells ( Figure 5B). However, the capacity of NNMT kd cells to use glutamine as mitochondrial fuel is slightly diminished compared to UT (diff = 14.2%, p = .08) and ctr.1 (diff = 17.6%, p = .08) cells ( Figure 5C). This is in line with our previous results that indicate defective glutamine metabolism in NNMT kd cells. Defective glutamine shuttling into mitochondrial oxphos might also be the reason for the impaired viability of NNMT kd cells. To further support this idea, we assessed the response of mitochondrial respiration and glycolysis in glucose-and glutamine-deprived cells to an injection of either glucose or glutamine. When starved cells were provided with glucose, the extracellular acidification rate was increased approximately sixfold, implying enhanced glycolytic activity, whereas oxygen consumption, representing mitochondrial respiration, was reduced to approximately 0.7-fold of baseline respiration ( Figure 5D). There were no differences between NNMT kd and control cells.

Glycolysis, oxidative phosphorylation and apoptosis pathways are upregulated in NNMT kd cells
To further analyse the effect of NNMT knockdown on gene expression levels, we next compared whole transcriptome gene expression of NNMT kd in 786-O cells versus control cells (ctr.1). NNMT is known as a phase II metabolic enzyme that methylates its substrates by the transfer of a methyl group from SAM. It has been proposed that high NNMT expression leads to the depletion of cellular SAM levels, generating a methylation sink in those cells, leading to altered epigenetic patterns and gene expression. 12 In general, we observed more downregulated genes in NNMT kd cells than in control cells ( Figure 6A). Notably, genes involved in glutamine metabolism, such as GLS and glutamate-ammonia ligase, or genes responsible for glutamine uptake were not differentially regulated in NNMT kd cells (Table S3). Next, we used GSEA to identify deregulated pathways in NNMT kd cells ( Figure 6B . Whether deregulation is a consequence of an altered methylation potential in NNMT kd cells or rather a consequence of impaired cellular function and fitness, or a combination of both, cannot be answered in our study.

Investigation of NNMT inhibition in ccRCC cell lines, primary cultures and ex vivo 3D models
From the in vitro data, we speculated that by inducing the expression of genes involved in glycolysis and oxphos, NNMT kd cells try to meet their energy demand in the face of impaired glutamine utilisation in mitochondrial oxphos. In this stressed scenario, NNMT-depleted cells might be more vulnerable to other therapies that target, for example, glycolysis, representing the other major energy production pathway. To test this hypothesis, we combined the NNMT inhibitor NNMTi 23 with the glucose analogue 2-DG, a potent inhibitor of glycolysis, or BPTES, an inhibitor of GLS, and monitored cell viability of 786-O and A498 cell lines, and primary ccRCC models (RCC1 and RCC2). The RCC models represent a primary tumour-derived ccRCC model and a model derived from a ccRCC lymph node metastasis, respectively. All models expressed NNMT ( Figure S8A). The specificity of the NNMT inhibitor was verified by measuring the NNMT-dependent metabolite 1-MNA in inhibitor-treated and control 786-O cells ( Figure  S9).
Treatment of all tested models with 50 or 100 μM NNMTi alone significantly reduced cell viability compared to DMSO-treated control cells (786-O: fc 50μM = 0.82, p 50μM = 5.29 × 10 -7 ; RCC2: fc 50μM = 0.80, p 50μM = .005; RCC1: fc 50μM = 0.78, p 50μM = .012) (Figures 6C-F and S10). A498 cells were the least sensitive, and only the maximal tested dose of 100 μM NNMTi led to a significant inhibition of cell viability (fc 100μM = 0.49, p 100μM = .021) ( Figure S10). In all tested models, the inhibitory effect of 50 μM NNMTi could be enhanced when combined with 1 mM 2-DG Notably, NNMT inhibition might not only inhibit cancer cells themselves but also impact the activation status of tumour-infiltrating T cells through its reaction product 1-MNA. 51 Along this line, the first data indicate that 1-MNA treatment of pre-stimulated T cells from healthy volunteers increases surface expression of the immune checkpoint PD1 on CD4+ and CD8+ T-cell populations ( Figure  S11).
To extend the findings of NNMT inhibition on cancer cells from 2D to more physiological 3D tumour models, we used patient-derived ex vivo ALI models of statistical significance, as determined by Student's t-test with Benjamini-Hochberg correction for multiple testing. (D) Changes in the extracellular acidification rate (ECAR) and OCR of NNMT kd and control cells in response to an injection of 10 mM glucose (final concentration). The responses to glucose were measured 20 min after injection. The detected differences were not statistically significant. (E) Changes in ECAR and OCR in response to an injection of 2 mM glutamine (final concentration). Again, the responses were measured 20 min after injection. (F) Impact of NNMT kd on the adenosine triphosphate (ATP) production rate and mitochondrial proton leakage. Significance levels, determined by Student's t-test with Benjamini-Hochberg correction for multiple testing, are given in the plot (ns: not significant; *p < .05; **p < .01)  three different ccRCC tumours. 22 Inhibitor-induced cytotoxicity was measured after treatment of the ALI models with 100 μM NNMTi for 24 h. We observed induced cytotoxicity of NNMTi-treated ALIs in two of the three tested models ( Figure 6G). All models expressed NNMT, although at varying levels ( Figure S8B).

DISCUSSION
With this study, we aimed to better understand the cellular function of NNMT in ccRCC and to elucidate its role as a potential target for therapy of metastatic RCC disease. NNMT has been shown to be highly expressed in several cancer entities, including ccRCC, 1,2 and expression was associated with more aggressive tumours and worse outcome in most studies (reviewed in Ref. 3 ). 52 In agreement, expression of NNMT was high in our patient cohort of primary ccRCC and the TCGA KIRC cohort, and mRNA expression correlated significantly with inferior patient survival. NNMT protein expression in cohort 1 showed the same trend of worse survival of patients with high NNMT-expressing tumours. In addition to primary tumour data, we report for the first time high expression of NNMT in ccRCC-derived metastases irrespective of the organ. The function NNMT may play in tumourigenesis is still a matter of discussion. Ulanovskaya et al. 12 proposed that by consuming SAM, NNMT generates a methylation sink in cells, which leads to altered histone methylation and expression of oncogenes. While no effect on DNA methylation was observed in this study, Jung et al. 13 reported increased DNA methylation and differentiation in NNMT-depleted glioblastoma stem cells. In another study, NNMT was reported to influence the methylation of tumour suppressors and oncogenes directly, thereby supporting cancer cell survival. 53 NNMT was also shown to regulate histone methylation in cancer-associated fibroblasts, supporting oncogenic remodelling of the metastasisassociated stroma. We did not observe prominent stromal NNMT expression in tissue sections of either primary ccRCC or ccRCC-derived metastasis ( Figure S12), indicating that, in ccRCC, tumour cell expression of NNMT plays the dominant role. This observation is supported by single-cell gene expressing data, showing the highest NNMT expression levels in the ccRCC tumour compartment. Eckert et al. and several other studies identified NNMT as a metastasis-associated gene, [54][55][56][57] possibly acting by inducing the expression of matrix-metalloproteinase 2 (MMP-2) 8 and maintaining dedifferentiated mesenchymal-like gene expression. 13,58 In agreement, we observed deregulated metastasis-associated genes in NNMT kd knockdown cells in our study, including MMP-13, laminin subunit gamma 2 (LAMC2) and alpha-1,6-mannosylglycoprotein 6-beta-Nacetylglucosaminyltransferase (MGAT5). NNMT has also been shown to regulate autophagy in liver and breast cancer cells 59,60 and to confer resistance to radiation and drug therapy. 53,[61][62][63] In our cohort of ccRCC-derived metastases, we observed slightly lower expression of NNMT in TKIpretreated tissues, which may be relevant for the therapeutic application of NNMT. However, this observation is based on the analysis of very few samples and needs further investigation.
In the present study, we show that NNMT knockdown impairs mitochondrial respiration and reduces the viability of RCC cells. We propose that the effect is caused by a defect in the shunting of glutamine through the TCA cycle to feed mitochondrial oxphos in NNMT kd cells. VHLdeficient RCC cells depend on extracellular glutamine for lipid synthesis, as highly active aerobic glycolysis prevents glucose from fueling the TCA cycle in these cells. 47,64 In addition, glutamine is used to produce GSH, which becomes increasingly important in advanced ccRCC to combat the accumulation of ROS in actively proliferating tumour cells. 17,50,[65][66][67] In our study, glutamine accumulation in NNMT kd cells did not affect the levels of reduced GSH. In contrast, we observed decreased levels of GSSG, which is in line with reduced oxidative stress due to impaired mitochondrial respiration in these cells. Importantly, in contrast to other recently published studies, 58,62,68 we did not observe an impact of NNMT knockdown on glycolytic function, the expression of individual glycolysisrelated genes, or sensitivity to glucose deprivation. We also did not observe altered expression of individual genes involved in glutamine metabolism in NNMT kd cells. On the pathway level, however, NNMT kd cells upregulate glycolysis, oxphos and ROS pathways, possibly in an attempt the piano package (see Section 2). (C-F) Cell viability of the clear cell renal cell carcinoma (ccRCC) cell lines 786-O and A498, the ccRCC tumour-derived primary model RCC2, and the ccRCC metastasis-derived primary model RCC1 treated with an inhibitor of nicotinamide-N-methyltransferase (NNMTi) alone or in combination with 2-deoxy-D-glucose (2-DG) or BPTES for 24 h. The shaded bars represent the additive inhibitory effects calculated by summing the inhibitory effects of the individual inhibitors. Bars represent the mean values ± standard deviation of three to five independent experiments. Significance was determined by Student's t-test with Benjamini-Hochberg correction for multiple comparisons. Significance levels are indicated in the graphs (ns: not significant; *p < .05; **p < .01; ***p < .001). (G) Cell cytotoxicity in three different 3D air-liquid interface (ALI) models of ccRCC treated with 100 μM NNMTi or DMSO (1:1000) as a control for 24 h. For each model, two independent experiments were performed in duplicate or triplicate wells. Individual fluorescence measurements, indicative of cell cytotoxicity, are plotted on top of box plots to compensate for the defect in mitochondrial respiration and cellular bioenergetics.
Taken together, we propose that NNMT supports oxphos in ccRCC tumours by regulating the shuttling of glutamine through the TCA cycle. Together with the utilisation of glutamine for GSH production and ROS neutralisation, this might underlie the glutamine avidity of ccRCC, providing cancer cells with sufficient energy for proliferation. It was recently proposed that, beyond ATP production, mitochondrial respiration is essential for cell proliferation by maintaining cytoplasmic aspartate levels. [69][70][71] In our study, we observed increased uptake of aspartate by NNMT kd cells, possibly as a consequence of impaired mitochondrial function. However, supplementing NNMT kd cells with aspartic acid could not rescue cell viability under glutamine-deprived conditions in our study. In addition, intracellular levels of aspartate in NNMT kd cells were higher than those in controls, leading us to the conclusion that aspartate deficiency does not seem to underlie the impaired viability of NNMT kd cells. In contrast, we observed impaired ATP production in NNMT kd cells. Hence, we speculated that inhibition of NNMT would render cells more vulnerable to glycolysis inhibition by 2-DG, since the combination of inhibitors would affect both energy production pathways. Indeed, dual inhibition of NNMT (using the inhibitor NNMTi 23 ) and glycolysis in both ccRCC cell lines (786-O and A498) and two 2D ccRCC primary models derived from tumour (RCC2) and metastasis (RCC1) led to strongly decreased cell viability. Whether the action of sirtuin proteins is involved in the regulation of NNMT-mediated glutamine shuttling by the induction of mitochondrial complex I activity and ATP synthesis, as suggested by Liu et al. 10 and Parsons et al. 11 in neuroblastoma, needs further experimental evaluation. We indeed observed slightly reduced sirtuin 1 (SIRT1) and growth arrest and DNA damage inducible alpha (GASS45A) expression in NNMT kd cells, although other SIRT1-regulated genes were not affected (data not shown).
In our study, we used the primary ccRCC-derived VHLdeficient 786-O cell line for mechanistic investigations, which is an accepted ccRCC in vitro model that is able to form ccRCC tumours in nude mice and maintain the characteristics of ccRCC tumours, such as vimentin and CD10 surface expression and secretion of high levels of vascular endothelial growth factor (VEGF). 72,73 Metabolically 786-O cells also resemble ccRCC tumours in terms of Warburg effect aerobic glycolysis, fatty acid and glutamine metabolism. 50 We further confirmed the influence of NNMT depletion on mitochondrial respiration in a ccRCC metastasis-derived primary cell culture (RCC1).
Notably, the effect of the NNMT inhibitor NNMTi was investigated in two ccRCC cell lines (786-O and A498), two patient-derived 2D models (RCC1 and RCC2) and three ex vivo patient-derived 3D ALI ccRCC models. It has already been demonstrated 22,74 that ALI models preserve the complex architecture of ccRCC, including even tumour infiltrating lymphocytes (TILs). Moreover, ex vivo tumour models have been shown to well recapitulate drug responses in patients 75,76 and are therefore a powerful system to study new drugs and drug combinations. NNMTi treatment-induced cytotoxicity was confirmed in two out of three patient-derived ccRCC ALI models, strongly corroborating the therapeutic potential of NNMT inhibition in vivo.
Thus, we propose that due to the broad expression of NNMT in primary ccRCC tumours and metastases, its association with patient survival, and its molecular functions, NNMT is a promising drug target in ccRCC. Small-molecule inhibitors of NNMT have been developed in recent years and are discussed as therapeutics for metabolic diseases, such as diabetes, obesity and fatty liver disease. 77 Preclinical studies have demonstrated beneficial effects of NNMT inhibition in obese mice. [78][79][80] Regarding NNMT inhibition in cancer, one study showed decreased tumour burden and cancer cell proliferation in an orthotropic mouse model of ovarian cancer treated with an NNMT inhibitor. 54 In addition, a newly published study demonstrated the tumourpromoting and immune-suppressing effects of 1-MNA, the NNMT-dependent metabolite of nicotinamide, in ovarian cancer. 51 Kilgour et al. showed that 1-MNA secreted by NNMT-expressing fibroblasts and cancer cells is taken up by T cells in the tumour microenvironment. In response, T cells secrete increased tumour necrosis factor-α and decreased interferon-γ, resulting in the promotion of tumour growth and reduced cytotoxicity. In agreement with the immune suppressive function, we observed a correlation of NNMT expression with the number of regulatory T cells in the tumour tissue of cohort 1 ( Figure  S13). Furthermore, the first data show that 1-MNA, alone and in combination with TGFβ, increases the amount of PD1-expressing CD4 T cells and, to a lesser extent, CD8 T cells. Although these data need further experimental evaluation, it seems likely that, similar to ovarian cancer, 1-MNA acts as an immune-suppressive metabolite in ccRCC, suggesting a dual effect of NNMT inhibition in ccRCC. As mentioned before, NNMT is highly expressed in the liver but also in normal kidneys and other organs, and therefore, the safety of NNMT inhibition in humans needs careful evaluation. The first data in animals did not report observable adverse effects of NNMT inhibition or NNMT knockdown. [79][80][81] In addition to molecular targeting, NNMT-derived peptides that are specifically presented by HLA molecules on ccRCC tumours could allow immunologic targeting of NNMT-expressing primary tumours and metastases. 14

CONCLUSIONS
Our study shows that NNMT is an important regulator of glutamine metabolism in ccRCC, with consequences for mitochondrial function and cellular fitness. NNMT inhibition impairs ccRCC metabolism alone or in combination with other agents and drives primary and metastatic cancer cells into cell death. Despite a growing repertoire of treatment options, advanced ccRCC is still not curable. To date, highly specific NNMT inhibitors have been developed without obvious safety issues in mice. Therefore, beyond its currently discussed role as a drug target in metabolic conditions, NNMT represents a promising new target for the treatment of ccRCC and potentially other cancer entities. 82